This article contains a proposal for an automatic human face detection method, that tries to join several theories proposed by different authors. The method is based on detection of shape features (eye pairs) and skin color. The method assumes certain circumstances and constraints, respectively. Therefore it is not applicable universally. Given the constraints, it is effective enough for applications where fast execution is required. Test results are given and at the end some directives for future work are discussed

Peer, Peter

Solina, Franc

English

ePrints.FRI

An Automatic Human Face Detection Method ⁄
Peter Peer, Franc Solina
Computer Vision Laboratory
Faculty of Computer and Information Science
University of Ljubljana
Tr•za•ska c. 25, SI-1001 Ljubljana, Slovenia
Tel. +386 (61) 1768 381, Fax. +386 (61) 1264 630
e-mail: peter.peer@fri.uni-lj.si, franc@fri.uni-lj.si
Abstract
This article contains a proposal for an automatic human face detection method, that
tries to join several theories proposed by difierent authors. The method is based
on detection of shape features (eye pairs) and skin color. The method assumes
certain circumstances and constraints, respectively. Therefore it is not applicable
universally. Given the constraints, it is efiective enough for applications where fast
execution is required. Test results are given and at the end some directives for
future work are discussed.
1 Introduction
Because of image-databases and \live" video information is growing more and more
widespread and expansive, their intelligent or automatic examining is becoming excep-
tionally important. People, i.e. human faces, are one of most common and very speciflc
objects, that we try to trace in images.
The system for automatic detection and recognition of human faces represents an
example of such an intelligent examining. Basic algorithm of the system is evident from
flgure 1.
This article deals with flrst two steps of the algorithm, i.e. face and feature segmen-
tation.
The purpose of automatic in-image face detection methods is obvious: their primary
goal is to segment image into regions that contain human faces or its parts and into
regions which can be - because they don’t represent a human face neither any of its
parts - neglected. Some possible applications for automatic face detection are:
⁄This work was supported by the Ministry of Science and Technology of Republic of Slovenia (Project
J2-8829).
Figure 1: Machine face recognition algorithm
† supervision and security applications,
† video-conference applications,
† animation of facial expressions,
† remote camera control applications,
† etc.
The majority of already developed methods have generally at least one of these two
problems:
† too high computational (time-, space-) complexity and/or
† too low efiectiveness.
At this point it is necessary to emphasize the fact, that automatic face detection as well
as most other automatic object-detection methods is a very pretentious task, especially
because of signiflcant sample variations, which can’t be easily analytically described with
parameters.
2 Face detection
There are a few distinct approaches to face detection [5, 7]:
† The top-down model-based approach assumes a difierent face model at difierent
coarse-to-flne scales. For e–ciency, the image is searched at the coarsest scale flrst.
Once a match is found, the image is searched at the next flner scale until the flnest
scale is reached. In general, only one model is assumed in each scale (usually in
the frontal-parallel view) and thus it is di–cult to extend this approach to multiple
views.
† The bottom-up feature-based approach searches the image for a set of facial features
and groups them into face candidates based on their geometric relationship. Though
this approach can be easily extended to multiple views, it is unable to work well
under difierent image conditions because the image structure of the facial features
vary too much to be robustly detected by the feature detectors.
† In texture-based approach faces are detected by examining the spatial distribution
of the gray-level information in the subimage (using Space Gray Level Dependency
(SGLD) matrices). This is again not easily extensible to multiple viewpoints.
† The neural network approach detects faces by subsampling difierent regions of the
image to a standard-sized subimage and then passing it through a neural network
fllter. In general, the algorithm performs very well for frontal-parallel faces but
performance deteriorates when extended to difierent views of the face. It is still not
possible to extend the algorithm to detect faces in proflle views.
† The color-based approach labels each pixel according to its similarity to skin color,
and subsequently labels each subregion as a face if it contains a large blob of skin
color pixels. It can cope with difierent viewpoint of faces but it is sensitive to skin
color and the face shape.
† Motion-based approaches use image subtraction to extract the moving foreground
from the static background. The face is then located by examining the silhouette
or the color of the difierence image. This approach will not work well when there
are a lot of moving objects in the image.
† At depth-based approach primary facial features are localized on the basis of facial
depth information. In the flrst step, pairs of stereo images containing frontal views
are sampled from the input video sequence. Then point correspondences over a
large disparity range are determined using a multiresolution hierarchical matching
algorithm. Finally, the facial features are located based on depth information.
3 Proposed face detection method
The method in question combines two common approaches, one based on features and
the other based on colors (see chapter 2). The two basic limitations of the method thus
originate from constraints of already mentioned approaches:
† input image must have high enough resolution; the face must be big enough and
† it is sensitive to the complexion (i.e. fair).
The basic idea of the algorithm is as follows: flnd in image all regions, which contain
possible candidates for an eye, then on the basis of geometric face characteristics try to
join two candidates into an eye pair and flnally, conflrm or refuse the face candidate using
complexion information.
The method was projected over a set of quite difierent pictures, i.e. the training set.
The goal of the method was to reach maximum classiflcation accuracy over the images,
which meet the following demands and constraints, respectively (beside already mentioned
two):
† real-time operation on - for the present - standard personal computer,
† plain background,
† uniform ambiental illumination,
† fair-complexion faces, which must be present in the image in their entirety (frontal
position) and
† faces, turned round for at most 30 degrees.
The method’s efiectiveness was tested over an independent set of images, i.e. the testing
set.
The basic principle of operation is shown on Fig. 2.
The method requires some thresholds, which play a crucial role for proper processing.
They are set quite loosely (tolerantly), but they become efiective as a sequence. All
thresholds were deflned experimentally using the training set.
Proposed face detection algorithm
Input: image in BMP format
Output: input image with denoted faces in it
Basic steps of algorithm are:
1. Firstly, completely unimportant colors are eliminated from image (those, which
can’t represent a face). All insigniflcant colors are replaced with white color.
2. Image is then converted into greyscale picture (PGM format).
Figure 2: Basic principles of proposed face detection method: 1. input image, 2. elimi-
nated insigniflcant colors, 3. image flltered with median fllter, 4. segmented white regions,
5. eliminated insigniflcant regions, 6. traced edges, 7. best possible circles within regions
of interest, 8. output image. Also next feature of the method is evident from the flg-
ure: because of greater distance of the faces in the background, the algorithm ascribe to
the latter lower probability based on the fact, that they have lower resolution and worse
illumination then the face in the foreground
3. It is flltered with a median fllter1.
4. With the help of a \Region growth" algorithm white regions are segmented.
5. Regions which can’t comprise an eye are eliminated as well.
6. Edges are traced in the image with signiflcant greys.
1The algorithm with median fllter blurs unimportant white regions (like, for an instance, regions, that
are consequences of difierent spots on the face), achieves greater compactness of interesting white regions
and eliminates small skin-like regions (which are within white regions).
7. Within preserved regions the algorithm searches for circles (eye candidates) using
Hough transform.
8. For each region the best possible circle is found.
9. Using geometric face characteristics the algorithm flnds partner circles representing
a pair of eyes.
10. For face candidate conflrmation, color information of the whole face is used. With
the help of this information the algorithm also (loosely) predicts the probability of
a face.
11. Finally, using some heuristic rules, the algorithm tries to eliminate falsely classifled
faces.2
4 Results
In order to get reliable results regarding algorithm’s efiectiveness, this method was tested
over an independent testing set. The set consists of images taken from two public image-
databases (PICS, M2VTS - [8]), few realistic face animations and a couple of celebrity
pictures.
A common feature of all images is their larger or lesser suitability for identiflcation
documents. Celebrity pictures are included in the test because they were taken by pro-
fessional photographers.
All images of the testing set do not meet the required constraints, as stated in Chapter
1, but exactly these images illustrate the proposed method’s drawbacks very well. Images
of faces which are less likely to be detected are:
† images of faces turned away from frontal position
† images with a complex background.
There are flve images of this kind in the testing set, in three out of these the method
successfully detected a face. The fact, that in two images faces were turned towards
proflle position, tells us that the method tolerates small deviations (see Fig. 3b) - on
the right side). In the third image the background wasn’t plain (see Fig. 3d) - the flrst
image). On the remaining two images the face was turned too much towards the proflle
position.
Table 1 presents testing results over the entire testing set (without the before men-
tioned two images that didn’t meet the demands, stated in chapter 1), Figs. 3 and
4 however, illustrate the method’s efiectiveness. False positives could be eliminated in
subsequent processing.
Generally speaking, glasses represent certain problem (e.g. compact frames or sun-
glasses), but hair-dress, moustache, beard and difierent mien don’t!
2The 11 steps of the algorithm are the result of the conceptual design of the algorithm.
Figure 3: Some positive results from the test set: a) from PICS database, b) from M2VTS
database, c) computer graphics face animations, d) images of professional photographs
complete test set - 44 images - 45 faces
number of detected faces number of hits
46 44
Table 1: Results of the method over the whole test set. There was 1 false positive and 1
false negative detected face
Figure 4: Negative results from the test set: on the left image the error is caused because
of hair color, which corresponds to complexion, while on the right image the error is
caused because an eye region and the background region align (the face is found on the
basis of the combination eye-eyebrow)
5 Conclusion and future work
From these result we can conclude, that taking into account speciflc constraints, clas-
siflcation accuracy achieved by this method is very high. These constraints hold if we
restrict ourselves on a particular image domain, which meets special requirements. In
other words, the method can be efiective enough for an application, which wouldn’t sufier
from algorithm constraints and which would require fast operation.
The proposed method could, for example, serve as a front part of a face recognition
system. Falsely detected faces could be eliminated using correspondence to the database
of already known faces. This was exactly the purpose of the method’s conception.
We are planning to test the method over a larger set of images; also over images, that
don’t contain faces. Such tests should help to detect weak points of the algorithm and
consequently give directions for algorithm upgrade.
References
[1] R. Feraud, O. Bernier, J.-E. Viallet, M. Collobert, D. Collobert, A Conditional
Mixture of Neural Networks for Face Detection, Applied to Locating and Tracking
an Individual Speaker, CAIL’97, Kiel, Germany, pp. 464-471, 1997.
[2] T. S. Jebara, A. Pentland, Parametrized Structure from Motion for 3D Adaptive
Feedback Tracking of Faces, IEEE CVPR Proceedings, pp. 144-150, 1997.
[3] E. Osuna, R. Freund, F. Girosi, Training Support Vector Machines: an Application
to Face Detection, IEEE CVPR Proceedings, pp. 130-136, 1997.
[4] H. A. Rowley, S. Baluja, T. Kanade, Neural Network-Based Face Detection, IEEE
Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 23-
38,1998.
[5] K. Sobottka, I. Pitas, A novel method for automatic face segmentation, facial feature
extraction and tracking, Signal processing: Image communication, 12, pp. 263-281,
1998.
[6] K.-K. Sung, T. Poggio, Example-Based Learning for View-Based Human Face De-
tection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20,
no. 1, pp. 39-51, 1998.
[7] K. C. Yow, R. Cipolla, Feature-based human face detection, Image and Vision Com-
puting, no. 15, pp. 713-735, 1997.
[8] Internet:
† http://pics.psych.stir.ac.uk/ - PICS Image Database,
† http://www.tele.ucl.ac.be/M2VTS/ - M2VTS Face Database.


An Automatic Human Face Detection Method

Abstract

Similar works

Full text

Available Versions

ePrints.FRI

University of Ljubljana Computer and Information Science ePrints.fri